Segmentation of Independently Moving Objects Using a Maximum-Likelihood Principle

Clauss, Martin; Bayerl, Pierre; Neumann, Heiko

doi:10.1007/3-540-30292-1_11

Cited by 5 publications

(4 citation statements)

References 6 publications

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“…Outlier detection. To detect outliers in ego-motion estimation, in particular IMOs, several methods were suggested, namely frameworks employing the EM algorithm [15,5], the Collinear Point Constraint [12] and the RANSAC algorithm [19]. In accordance to the conclusion of Torr's thesis, who found that the RANSAC algorithm performs best in motion segmentation and outlier detection, we chose RANSAC to achieve robust ego-motion estimation.…”

Section: Discussionmentioning

confidence: 99%

“…A key functionality of the subspace method is the possibility to cluster ego-motion and motion of IMOs. More robust approaches assume noisy flow estimates besides IMOs when estimating ego-motion with the EM algorithm [16,5]. Generally, the EM algorithm uses an iterative computational scheme and in each iteration the evaluation of the method estimating ego-motion is required.…”

Section: Motivationmentioning

confidence: 99%

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An Efficient Linear Method for the Estimation of Ego-Motion from Optical Flow

Raudies

Neumann

2009

Lecture Notes in Computer Science

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Approaches to visual navigation, e.g. used in robotics, require computationally efficient, numerically stable, and robust methods for the estimation of ego-motion. One of the main problems for egomotion estimation is the segregation of the translational and rotational component of ego-motion in order to utilize the translation component, e.g. for computing spatial navigation direction. Most of the existing methods solve this segregation task by means of formulating a nonlinear optimization problem. One exception is the subspace method, a wellknown linear method, which applies a computationally high-cost singular value decomposition (SVD). In order to be computationally efficient a novel linear method for the segregation of translation and rotation is introduced. For robust estimation of ego-motion the new method is integrated into the Random Sample Consensus (RANSAC) algorithm. Different scenarios show perspectives of the new method compared to existing approaches.

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Section: Discussionmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

An Efficient Linear Method for the Estimation of Ego-Motion from Optical Flow

Raudies

Neumann

2009

Lecture Notes in Computer Science

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“…Other methods solve segmentation and heading estimation with the expectation and maximization (EM) algorithm (MacLean et al, 1994; Clauss et al, 2005). The expectation step estimates the probability of a data point as being generated by a motion model.…”

Section: Discussionmentioning

confidence: 99%

Modeling heading and path perception from optic flow in the case of independently moving objects

Raudies

Neumann

2013

Front. Behav. Neurosci.

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Humans are usually accurate when estimating heading or path from optic flow, even in the presence of independently moving objects (IMOs) in an otherwise rigid scene. To invoke significant biases in perceived heading, IMOs have to be large and obscure the focus of expansion (FOE) in the image plane, which is the point of approach. For the estimation of path during curvilinear self-motion no significant biases were found in the presence of IMOs. What makes humans robust in their estimation of heading or path using optic flow? We derive analytical models of optic flow for linear and curvilinear self-motion using geometric scene models. Heading biases of a linear least squares method, which builds upon these analytical models, are large, larger than those reported for humans. This motivated us to study segmentation cues that are available from optic flow. We derive models of accretion/deletion, expansion/contraction, acceleration/deceleration, local spatial curvature, and local temporal curvature, to be used as cues to segment an IMO from the background. Integrating these segmentation cues into our method of estimating heading or path now explains human psychophysical data and extends, as well as unifies, previous investigations. Our analysis suggests that various cues available from optic flow help to segment IMOs and, thus, make humans' heading and path perception robust in the presence of such IMOs.

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“…Existing motion detection schemes exploit a subset of the above constraints either directly or indirectly. A popular scheme is the angle criterion [8,24] which uses the direction of optical flow vectors. When moving purely translational toward the scene, all flow vectors are parallel to the corresponding epipolar lines and point away from the epipole (focus of expansion).…”

Section: Monocular Visionmentioning

confidence: 99%

Moving Object Segmentation Using Optical Flow and Depth Information

Klappstein

Vaudrey

Rabe

et al. 2009

Lecture Notes in Computer Science

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This paper discusses the detection of moving objects (being a crucial part of driver assistance systems) using monocular or stereoscopic computer vision. In both cases, object detection is based on motion analysis of individually tracked image points (optical flow), providing a motion metric which corresponds to the likelihood that the tracked point is moving. Based on this metric, points are segmented into objects by employing a globally optimal graph-cut algorithm. Both approaches are comparatively evaluated using real-world vehicle image sequences.

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Segmentation of Independently Moving Objects Using a Maximum-Likelihood Principle

Cited by 5 publications

References 6 publications

An Efficient Linear Method for the Estimation of Ego-Motion from Optical Flow

An Efficient Linear Method for the Estimation of Ego-Motion from Optical Flow

Modeling heading and path perception from optic flow in the case of independently moving objects

Moving Object Segmentation Using Optical Flow and Depth Information

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